Temperature variable optimization for precision machine tool thermal error compensation on optimal threshold

Zhang, Ting; Ye, Wenhua; Liang, Ruijun; Lou, Peihuang; Yang, Xiaolan

doi:10.3901/cjme.2013.01.158

Cited by 27 publications

(10 citation statements)

References 6 publications

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“…The common advantages of the existing mathematic statistics method include the following: a large number * Chengxin Zhang qfzcx_sd@163.com Feng Gao gf2713@126.com of sensors can be arranged on a machine tool, and several optimal measurement points can then be selected from the arranged sensors through a statistical analysis [4][5][6][7][8][9][10][11][12]. However, there are certain disadvantages of this type of method:…”

Section: Mathematical Statistics Methodsmentioning

confidence: 99%

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Zhang

Gao

Zhenhua

et al. 2015

Int J Adv Manuf Technol

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When a thermal error model of a machine tool is established, selecting the appropriate temperature measurement point is a very difficult problem. This paper proposes a novel method for constructing a linear virtual temperature. The proposed method can overcome the problem of selecting the temperature measurement point. First, temperaturethermal expansion hysteresis characteristics are used to divide the temperature measurement points into two groups. Each temperature variable is then chosen through a principal component analysis (PCA). Finally, using two temperaturevariable weights and the correlative coefficient thermal displacement as the maximum optimal indexes, two temperatureweighted coefficients are calculated, and a linear virtualtemperature variable related to the thermal error linearity is then formed. In establishing the proposed thermal error model, the linear virtual temperature formed can serve as a system input variable. The proposed method was tested on a threeaxis milling machine to determine the spindle Z-axial thermal error, and the results show that the root mean square error (RMSE) is reduced by 11 % and the sum of the squares of the error (SSE) is reduced by 39 % in comparison with a direct application of a temperature variable when establishing such a model. In the proposed method, only two temperature measurement points are used to establish a model, through which the complexity in determining the optimal measurement points through a traditional method, along with the number of temperature measurement points required, is greatly reduced.

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Section: Mathematical Statistics Methodsmentioning

confidence: 99%

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Zhang

Gao

Zhenhua

et al. 2015

Int J Adv Manuf Technol

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show abstract

“…Thermally induced error caused by environment or internal heat sources can contribute more than 50% to the overall geometrical error of machined workpieces [4]. Therefore, much research focusing on thermal error reduction and compensation of machine tools has been carried out [5][6][7][8][9]. Due to the large structure and complex distribution of heat sources in machine tools, the accuracy of a heavy-duty machine tool is much sensitive to the variation of temperature field over the machine structure.…”

Section: Introductionmentioning

confidence: 99%

Real-time measurement of temperature field in heavy-duty machine tools using fiber Bragg grating sensors and analysis of thermal shift errors

et al. 2015

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“…Han, et al use fuzzy c-means algorithms to select a key TMP within a group; they select 4 key TMPs from 32 in the case study (Han et al, 2012). Zhang, et al present an optimal clustering threshold for key TMPs selection; they select 7 key TMPs out of 29 in the case study (ZHANG et al, 2013). Although the method is "practical and effective" at handling correlation among TMPs, it is very difficult to determine an appropriate threshold, which is critical in the method.…”

Section: Introductionmentioning

confidence: 99%

“…And these parameters can affect the number of selected TMPs. And the values of these parameters are mainly determined by researchers experience, although the number of selected TMPs may be crucial to thermal error model (ZHANG et al, 2013). Too less TMPs may lead to poor prediction accuracy while too many may have a negative influence on a model's robustness (Abdulshahed et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Selection of key temperature measuring points for thermal error modeling of CNC machine tools

Liu¹,

Chen²,

Lou³

et al. 2018

JAMDSM

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Thermal errors account for more than 40% of the machining errors in CNC machine tools, and even reach 70% in precision and ultra-precision machine tools. One approach to reducing thermal errors is to build thermal error prediction models by monitoring the temperature field of machine tools with the data-driven modeling approaches. Usually the data-driven approaches have over-fitting and under-fitting problems. The prediction performances of the data-driven models are greatly dependent on the input of the models, namely the number of temperature measuring points (TMPs) and their locations. In this paper, a selection method of key TMPs is presented to improve the accuracy and generalization of predictive models. In this selection method, correlation analysis is used to eliminate the uncorrelated or weakly correlated TMPs to thermal errors at first; then the Minimum Redundancy Maximum Relevance (MRMR) is presented to narrow the searching scope of TMPs; finally Wrapper method is used to test the candidate set of TMPs with cross-validation accuracy to find the key TMPs. To validate this method, a test platform is built on the CNC gantry drilling machine ZK5540A. 56 Fiber Bragg Grating (FBG) temperature sensors are amounted on the body, column, spindle, and base of this machine. 7 key TMPs was selected from the 56 ones with this method. And Multiple linear regression (MLR) approach is used to build the thermal error prediction model with the 7 key TMPs, 56 TMPs and key TMPs selected by Miao's method (Miao et al., 2014) respectively. The result shows that the prediction model built with MLR using the 7 key TMPs is much more accurate and has stronger generalization.

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Temperature variable optimization for precision machine tool thermal error compensation on optimal threshold

Cited by 27 publications

References 6 publications

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Real-time measurement of temperature field in heavy-duty machine tools using fiber Bragg grating sensors and analysis of thermal shift errors

Selection of key temperature measuring points for thermal error modeling of CNC machine tools

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